Data Mining and Knowledge Engineering

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Big Data Analytics Model for Comprehensive Stock Market Anatomization


Affiliations
1 Ganpat University, Gujarat, India
     

   Subscribe/Renew Journal


Prediction of future value specific stock is most complicated job. Researchers have proposed several processes, methods to evaluate the intrinsic value of stock largely categorized in fundamental, technical and sentimental analysis of the data and its changing pattern. Scope to perform in-depth end to end analysis of real time stock market and related data is increased along with development of data mining and analytics techniques. The proposed model of big data analytics integrate traditional approaches of fundamental, technical and sentimental analysis with extensive data sources available on stock exchanges, trade data stream (stock quotes) and traders sentiments (social media and comments on websites), Data needs to be analyze is  available in structured, semi structured and unstructured formats. This model also describes gathering, mapping and processing of stock market data for anatomization using the big data components like flume, hive, sqoop for different need of data processing for analytics and decision purpose.

Keywords

Analytics, Big Data, Prediction, Stock Market, Stream Data.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Gay Jr, Robert D. Effect of macroeconomic variables on stock market returns for four emerging economies: A vector regression model for Brazil, Russia, India, and China. Nova Southeastern University, 2008.

  • Patel, Hiral R., Amit B. Suthar, and Satyen M. Parikh. "A Proposed Prediction Model for Forecasting the Financial Market Value According to Diversity in Factor." International Journal of Computer Technology and Applications 5.1 (2014): 131.

  • Baker, Malcolm, and Jeffrey Wurgler. "Investor sentiment in the stock market." The Journal of Economic Perspectives 21.2 (2007): 129-151.

  • Suthar, Mr Amit B., Ms Hiral R. Patel, and Dr Satyen M. Parikh. "A Comparative Study on Financial Stock Market Prediction Models." The International Journal of Engineering and Science (IJES) 1.2 (2012): 188-191.

  • Patel, Hiral R., Satyen M. Parikh, and Dhara N. Darji. "Prediction model for stock market using news based different Classification, Regression and Statistical Techniques: (PMSMN)." ICT in Business Industry & Government (ICTBIG), International Conference on. IEEE, 2016.

  • Roberts, Harry V. "Stock Market “Patterns” And Financial Analysis: Methodological Suggestions." The Journal of Finance 14.1 (1959): 1-10.

  • Patel, Hiral, and Satyen Parikh. "Comparative analysis of different statistical and neural network based forecasting tools for prediction of stock data." Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies. ACM, 2016.

  • Parikh, Satyen M. Analysis and modeling a distributed co-operative multi agent system for scaling-up business intelligence. Diss. Saurashtra University, 2008.

  • Bollen, Johan, Huina Mao, and Xiaojun Zeng. "Twitter mood predicts the stock market." Journal of computational science 2.1 (2011): 1-8.

  • Patel, Hiral R., and Satyen Parikh. "Comparative Analytical Study for News Text Classification Techniques Applied for Stock Market Price Extrapolation." International Conference on Smart Trends for Information Technology and Computer Communications. Springer, Singapore, 2016.

  • McAfee, Andrew, et al. "Big data." The management revolution. Harvard Bus Rev 90.10 (2012): 61-67.

  • Mayer-Schönberger, Viktor, and Kenneth Cukier. Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt, 2013.

  • Boyd, Danah, and Kate Crawford. "Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon." Information, communication & society 15.5 (2012): 662-679.

  • Lazer, David, et al. "The parable of Google Flu: traps in big data analysis." Science 343.6176 (2014): 1203-1205.

  • Fan, Wei, and Albert Bifet. "Mining big data: current status, and forecast to the future." ACM sIGKDD Explorations Newsletter 14.2 (2013): 1-5.

  • Gantz, John, and David Reinsel. "The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east." IDC iView: IDC Analyze the future 2007.2012 (2012): 1-16.


Abstract Views: 366

PDF Views: 2




  • Big Data Analytics Model for Comprehensive Stock Market Anatomization

Abstract Views: 366  |  PDF Views: 2

Authors

Satyen M. Parikh
Ganpat University, Gujarat, India
Dhara N. Darji
Ganpat University, Gujarat, India

Abstract


Prediction of future value specific stock is most complicated job. Researchers have proposed several processes, methods to evaluate the intrinsic value of stock largely categorized in fundamental, technical and sentimental analysis of the data and its changing pattern. Scope to perform in-depth end to end analysis of real time stock market and related data is increased along with development of data mining and analytics techniques. The proposed model of big data analytics integrate traditional approaches of fundamental, technical and sentimental analysis with extensive data sources available on stock exchanges, trade data stream (stock quotes) and traders sentiments (social media and comments on websites), Data needs to be analyze is  available in structured, semi structured and unstructured formats. This model also describes gathering, mapping and processing of stock market data for anatomization using the big data components like flume, hive, sqoop for different need of data processing for analytics and decision purpose.

Keywords


Analytics, Big Data, Prediction, Stock Market, Stream Data.

References